Tuyển tập các báo cáo nghiên cứu về sinh học được đăng trên tạp chí sinh học Journal of Biology đề tài: A Monte-Carlo algorithm for maximum likelihood estimation of variance | 329 Genet Sei Evol 1996 28 329-343 Elsevier INRA Original article A Monte-Carlo algorithm for maximum likelihood estimation of variance components s Xu1 WR Atchley 2 1 Department of Botany and Plant Sciences University of California Riverside CA 92521 2 Department of Genetics North Carolina State University Raleigh NC 27695 USA Received 30 January 1995 accepted 24 May 1996 Summary - A new algorithm for finding maximum likelihood ML solutions to variance components is introduced. This algorithm first treats random effects as fixed then expresses the pseudo-fixed effects as linear transformations of a set of standard normal deviates which eventually are integrated out numerically through Monte-Carlo simulation. An iterative algorithm is employed to estimate the standard deviation rather than the variance of the random effects. This method is conceptually simple and easy to program because repeated updating and inverting the variance-covariance matrix of data is not required. It is potentially useful for handling large data sets and data that are not normally distributed. maximum likelihood restricted maximum likelihood variance component MonteCarlo mixed model Resume Un algorithme de Monte-Carlo pour estimer des composantes de variance par le maximum de vraisemblance. Un nouvel algorithme pour résoudre le maximum de vraisemblance de composantes de variance est présenté. Cet algorithme traite d abord les effets aléatoires comme des effets fixes puis exprime ces pseudo-effets fixes sous la forme de transformations linéaires d un ensemble de variables normales centrées réduites. Celles-ci sont ensuite éliminées par integration à I aide d un processus numérique de Monte-Carlo. Un algorithme iteratif est employe pour estimer I ecart type et non la variance des effets aléatoires. Cette methode est simple conceptuellement et facile à programmer parce que des inversions de la matrice de variance-covariance des données répétées à chaque iteration ne sont plus nécessaires. La
